Cross-Level Measurement Invariance in School Climate Surveys: Implications for Policy and Practice
Measures of classroom and school environments are central to policy efforts that assess school and teacher quality. These measures are often formed by aggregating individual survey responses to form group-level measures, and assume invariance across levels (i.e. the same measurement model holds across the individual and group levels). In this work I explore the tenability of this assumption by examining the multilevel factor structure of the Working Conditions Survey, which assesses teacher perceptions of school climate. The data come from a statewide 2008 administration of the Working Conditions Survey in North Carolina. The results illustrate the consequences of using common factor analytic methods that assume cross-level invariance when this assumption is not met in the data: distorted perceptions of factorial structure, which can influence inferences about the relationship between working conditions and teacher mobility.
Jonathan Schweig (MA '02) is a doctoral candidate in Advanced Quantitative Methods at the Graduate School of Education and Information Studies at the University of California, Los Angeles. His research interests lie at the intersection of quantitative methodology and education policy, specifically as it relates to the measurement of classroom practice and teacher quality, the development and implementation of teacher evaluation policies, and the development of policies to reduce teacher turnover. His recent publications investigate the application of generalizability theory, multilevel modeling, and latent variable modeling to these issues, with a focus on the implications of methodological choices for inferences about individual teachers and schools, and the relationships between variables at different levels of analysis.
